1. Field of the Invention
The present invention relates to image encoding and decoding, and more particularly, to an image encoding and/or decoding apparatus for encoding an image with a quantization parameter determined by using an allowable noise having no subjective loss, and decoding the encoded bitstream, and a method thereof.
2. Description of the Related Art
Until now, many trials to apply a human visual system to image encoding have been made. These efforts are based on that human visual ability evaluating the picture quality of a restored image does not match a simple perspective of signal processing. However, in the image encoding field, many approaches are beginning their research still with an assumption that noise which a human being feels is the same as a signal noise. In particular, a mean square error (MSE) can be said to be a signal noise that is most widely used, and the degree of picture quality of an image restored according to the MSE is numerically evaluated by a peak signal to noise ratio (PSNR).
As a visual characteristic index generally widely used, there is a just noticeable distortion (JND) value. The JND value is calculated by considering a relative change degree of neighboring pixel values for one pixel value, and models a characteristic that a human being cannot recognize a big noise in a complicated background. In encoding of an image, a JND value obtained in each pixel is used to additionally adjust a quantization parameter in a quantization process. Through this process, a region to which the human visual characteristic is sensitive is made to generate less noise, and a region to which the human visual characteristic is insensitive is made to generate more noise so that the compression ratio can be increased.
Meanwhile, in addition to the JND value, the human visual characteristic in frequency domain can be considered. For this, a modulation transfer function of the human visual characteristic is empirically obtained from a variety of test images, and by using this, a quantization weight value of each frequency band for a discrete cosine transform (DCT) coefficient is extracted.
These methods applying the human visual characteristic to image encoding by using the JND value or the quantization weight have many limitations. When the quantization weight is used, a problem that the weight value in relation to only the frequency component is uniformly applied arises. Also, the applying of the JND value has a problem because the human visual characteristic does not rely only on the relative value to the neighboring pixel values. Furthermore, since the JND value should be calculated for each pixel value, a quantization parameter that is basically provided to control the quantity of bits should be modified in units of pixels. Accordingly, separate information before modification should be transferred to a decoder, or a JND value in the decoder should be calculated as the same value as the JND value in the encoder. At this time, the basic problem is that the decoder has only a reproduced value, and therefore the encoder should calculate the JND value with a reproduced value as in the decoder.
Documents related to image encoding applying the human visual characteristic include Bowonkoon Chitprasrt and K. R. Rao's article, “Human Visual Weighted Progressive Image Transmission” (IEEE Trans. Communications, vol. 38, pp. 1040-1044, July, 1990), ISO/IEC 11172-2, Information Technology-Coding of Moving Pictures and Associated Audio for Digital Storage Media at Up to About 1.5 Mbits/s Part 2: Video, 1993, and King N. Ngan, Kin S. Leong, and H. Singh's article, “Adaptive Cosine Transform Coding of Image in Perceptual Domain” (IEEE Trans. Acoustics, Speech and Signal Processing, vol. 37, pp. 1743-1750, November 1989).